At least two goals of creating pricing models are to analyze price behavior and to apply that analysis to real time transactions in order to preserve what are increasingly becoming narrower profit margins in complex transactions. Systems like, for example, SAP™, attempt to manage and control business processes using objective data in order to gain enterprise efficiencies. By manipulating objective data, these systems offer consistent metrics upon which businesses may make informed decisions and policies regarding the viability and direction of their products and services. However, in many cases, the decisions and policies may be difficult to procure as a result of the volume and organization of relevant data and may be difficult to implement as both temporal restraints and approval processes may inhibit rapid deployment of valuable information.
For example, referring to FIG. 1, FIG. 1 is a simplified graphical representation of an enterprise pricing environment. Several example databases (104-120) are illustrated to represent the various sources of working data. These might include, for example, Trade Promotion Management (TPM) 104, Accounts Receivable (AR) 108, Price Master (PM) 112, Inventory 116, and Sales Forecasts 120. The data in those repositories may be utilized on an ad hoc basis by Customer Relationship Management (CRM) 124, and Enterprise Resource Planning (ERP) 128 entities to produce and post sales transactions. The various connections 148 established between the repositories and the entities may supply information such as price lists as well as gather information such as invoices, rebates, freight, and cost information.
The wealth of information contained in the various databases (104-120) however, is not “readable” by executive management teams due in part to accessibility and in part to volume. That is, even though data in the various repositories may be related through a Relational Database Management System (RDMS), the task of gathering data from disparate sources can be complex or impossible depending on the organization and integration of legacy systems upon which these systems may be created. In one instance, all of the various sources may be linked to a Data Warehouse 132 by various connections 144. Typically, data from the various sources may be aggregated to reduce it to a manageable or human comprehensible size. Thus, price lists may contain average prices over some selected temporal interval. In this manner, data may be reduced. However, with data reduction, individual transactions may be lost. Thus, CRM 124 and ERP 128 connections to an aggregated data source may not be viable.
Analysts 136, on the other hand, may benefit from aggregated data from a data warehouse. Thus, an analyst 136 may compare average pricing across several regions within a desired temporal interval to develop, for example, future trends in pricing across many product lines. An analyst 136 may then generate a report for an executive committee 140 containing the findings. An executive committee 140 may then, in turn, develop policies that drive pricing guidance and product configuration suggestions based on the analysis returned from an analyst 136. Those policies may then be returned to CRM 124 and ERP 128 entities to guide pricing activities via some communication channel 152 as determined by a particular enterprise.
As can be appreciated, a number of complexities may adversely affect this type of management process. First, temporal setbacks exist at every step of the process. For example, a CRM 124 may make a sale. That sale may be entered into a sales database 120, and INV database 116, and an AR database 108. The entry of that data may be automatic where sales occur at a network computer terminal, or may be entered in a weekly batch process thus introducing a temporal setback. Another example of a temporal setback is time-lag introduced by batch processing data stored to a data warehouse resulting in weeks-old data that may not be timely for real-time decision support. Still other temporal setbacks may occur at any or all of the transactions illustrated in FIG. 1 that may ultimately render results untimely at best and irrelevant at worst. Thus, the relevance of an analyst's 136 original forecasts may expire by the time the forecasts reach the intended users. Still further, the usefulness of any pricing guidance and product configuration suggestions developed by an executive committee 140 may also have long since expired leaving a company exposed to lost margins.
As such, methods of displaying and using predictive structured data, integrating that data into coherent and relevant business policies such as pricing guidance and product configuration suggestions, and deploying those policies in a timely and efficient manner may be desirable to achieve price modeling efficiency and accuracy.
In view of the foregoing, Systems and Methods for Margin-Sensitive Price Adjustments in an Integrated Price Management System are disclosed.